Publications

• Duan, C. and Jiang, Y. (2025), "Cluster analysis of longitudinal profiles for compositional count data," Annals of Applied Statistics, Link.
• Palmer, E., Hammer, A., Sharpton, T. J., and Jiang, Y. (2025), "A group penalization framework for detecting time-lagged microbiota-host associations," Frontiers in Genetics, 16:1504443. Link.
• Mei, M., Yu, T., and Jiang, Y. (2024), "Asymptotic uncertainty of false discovery proportion," Biometrics, 80, ujae015. Link.
• Li, K., Song, C., and Jiang, Y. (2024), "Data integration of multiple genome-wide association studies under group homogeneous structure," Statistics and Its Interface, 17, 517-532. Link.
• Wen, C., Wang, Q., and Jiang, Y. (2023), "Stability approach to regularization selection for reduced-rank regression," Journal of Computational and Graphical Statistics, 32, 974-984. Link.
Tian, C., Jiang, D., Hammer, A., Sharpton, T. J., and Jiang, Y. (2023), "Compositional graphical lasso resolves the impact of parasitic infection on gut microbial interaction networks in a zebrafish model," Journal of the American Statistical Association, 118, 1500-1514. Link.
Jiang, D., Sharpton, T. J., and Jiang, Y. (2021), "Microbial interaction network estimation via bias-corrected graphical lasso," Statistics in Biosciences, 13, 329-350. Link.
Li, K., Mei, M., and Jiang, Y. (2021), "Grouped variable selection with prior information via the prior group bridge method," Statistics and Its Interface, 14, 211-227. Link.
Jiang, D., Armour, C. R., Hu, C., Mei, M., Tian, C., Sharpton, T. J., and Jiang, Y. (2019), "Microbiome multi-omics network analysis: statistical considerations, limitations, and opportunities," Frontiers in Genetics, 10, 995. Link.
Liu, S., Jiang, Y., and Yu, T. (2019), "Modelling RNA-Seq data with a zero-inflated mixture Poisson linear model," Genetic Epidemiology, 43, 786-799. Link.
Jiang, Y., He, Y., and Zhang, H. (2016), "Variable selection with prior information for generalized linear models via the prior LASSO method," Journal of the American Statistical Association, 111, 355-276. Link.
Jiang, Y., Li, N., and Zhang, H. (2014), "Identifying genetic variants for addiction via propensity score adjusted generalized Kendall's tau," Journal of the American Statistical Association, 109, 905-930. Link.
Jiang, Y. and Zhang, C. M. (2013), "High-dimensional regression and classification under a class of convex loss functions," Statistics and Its Interface, 6, 285-299. Link.
Zhu, W., Jiang, Y., and Zhang, H. (2012), "Nonparametric covariate-adjusted association tests based on the generalized Kendall's tau," Journal of the American Statistical Association, 107, 1-11. Link.
Jiang, Y., and Zhang, H. (2011), "Propensity score-based nonparametric test revealing genetic variants underlying bipolar disorder," Genetic Epidemiology, 35, 125-132. Link.
Zhang, C. M., Jiang, Y., and Chai, Y. (2010), "Penalized Bregman divergence for large dimensional regression and classification," Biometrika, 97, 551-566. Link.
Zhang, C. M., Jiang, Y., and Shang, Z. (2009), "New aspects of Bregman divergence in regression and classification with parametric and nonparametric estimation," The Canadian Journal of Statistics, 37, 119-139. Link.
Zhang, C. M., Jiang, Y., and Yu, T. (2007), "A comparative study of one-level and two-level semiparametric estimation of hemodynamic response function for fMRI data," Statistics in Medicine, 26, 3845-3861. Link.
Zhang, C. M., Fu, H., Jiang, Y., and Yu, T. (2007), "High-dimensional pseudo logistic regression and classification with applications to gene expression data," Computational Statistics & Data Analysis, 52, 452-470. Link.